Workshop
Machine Learning for Systems
Neel Kant 路 Martin Maas 路 Azade Nova 路 Benoit Steiner 路 Xinlei XU 路 Dan Zhang
Sat 3 Dec, 6:30 a.m. PST
Machine Learning (ML) for Systems is an important direction for applying ML in the real world. It has been shown that ML can replace long standing heuristics in computer systems by leveraging supervised learning and reinforcement learning (RL) approaches. The computer systems community recognizes the importance of ML in tackling strenuous multi-objective tasks such as designing new data structures 1, integrated circuits 2,3, or schedulers, as well as implementing control algorithms for applications such as compilers 12,13, databases 8, memory management 9,10 or ML frameworks 6.
General Workshop Direction. This is the fifth iteration of this workshop. In previous editions, we showcased approaches and frameworks to solve problems, bringing together researchers and practitioners at NeurIPS from both ML and systems communities. While breaking new grounds, we encouraged collaborations and development in a broad range of ML for Systems works, many later published in top-tier conferences 6,13,14,15,16,17,18. This year, we plan to continue on this path while expanding our call for paper to encourage emerging works on minimizing energy footprint, reaching carbon neutrality, and using machine learning for system security and privacy.
Focusing the Workshop on Unifying Works. As the field of ML for Systems is maturing, we are adapting the focus and format of the workshop to evolve with it. The community has seen several efforts to consolidate different subfields of ML for Systems 4, 5, 6, 7. However, such efforts need more support. To boost recent advances in shared methodology, tools, and frameworks, this year we will welcome submissions presenting datasets, simulators, or benchmarks that can facilitate research in the area.
Schedule
Sat 6:30 a.m. - 6:40 a.m.
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Opening Remarks
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Opening Remarks
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SlidesLive Video |
馃敆 |
Sat 6:40 a.m. - 7:30 a.m.
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Jeff Dean - Invited Talk
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Talk
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Jeff Dean 馃敆 |
Sat 7:30 a.m. - 8:15 a.m.
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Dawn Song - Invited Talk
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Talk
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SlidesLive Video |
Dawn Song 馃敆 |
Sat 8:20 a.m. - 8:50 a.m.
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Poster Session | Coffee Break
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馃敆 |
Sat 8:55 a.m. - 9:05 a.m.
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A code superoptimizer through neural Monte-Carlo tree search
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Spotlight
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SlidesLive Video |
Wenda Zhou 路 Olga Solodova 路 Ryan Adams 馃敆 |
Sat 9:10 a.m. - 9:20 a.m.
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Predicting Network Buffer Capacity for BBR Fairness ( Spotlight ) > link | Ibrahim Umit Akgun 路 Santiago Vargas 路 Andrew Burford 路 Michael McNeill 路 Michael Arkhangelskiy 路 Aruna Balasubramanian 路 Anshul Gandhi 路 Erez Zadok 馃敆 |
Sat 9:25 a.m. - 9:35 a.m.
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HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression ( Spotlight ) > link | Jiaqi Gu 路 Ben Keller 路 Jean Kossaifi 路 Anima Anandkumar 路 Brucek Khailany 路 David Pan 馃敆 |
Sat 9:45 a.m. - 9:55 a.m.
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Learning to Drive Software-Defined Storage
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Spotlight
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link
SlidesLive Video |
Jian Huang 路 Daixuan Li 路 Jinghan Sun 馃敆 |
Sat 11:05 a.m. - 11:20 a.m.
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Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration
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Spotlight
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link
SlidesLive Video |
Srivatsan Krishnan 路 Natasha Jaques 路 Shayegan Omidshafiei 路 Dan Zhang 路 Izzeddin Gur 路 Vijay Janapa Reddi 路 Aleksandra Faust 馃敆 |
Sat 11:30 a.m. - 11:45 a.m.
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Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
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Spotlight
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link
SlidesLive Video |
Benjamin Fuhrer 路 Yuval Shpigelman 路 Chen Tessler 路 Shie Mannor 路 Gal Chechik 路 Eitan Zahavi 路 Gal Dalal 馃敆 |
Sat 11:45 a.m. - 12:15 p.m.
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Poster Session | Coffee Break
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馃敆 |
Sat 12:15 p.m. - 1:00 p.m.
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Steve Keckler - Invited Talk: Applying ML to Practical System Design
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Talk
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SlidesLive Video |
Stephen Keckler 馃敆 |
Sat 1:05 p.m. - 1:50 p.m.
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Newsha Ardalani - Invited Talk
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Talk
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SlidesLive Video |
Newsha Ardalani 馃敆 |
Sat 1:55 p.m. - 2:30 p.m.
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Riyadh Baghdadi - Invited Talk
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Talk
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SlidesLive Video |
Riyadh Baghdadi 馃敆 |
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Towards Continually Learning Application Performance Models
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Poster - Recorded Presentation
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link
SlidesLive Video |
Ray Sinurat 路 Sandeep Madireddy 路 Anurag Daram 路 Haryadi Gunawi 路 Robert Ross 馃敆 |
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Learning to Drive Software-Defined Storage ( Poster ) > link | Jian Huang 路 Daixuan Li 路 Jinghan Sun 馃敆 |
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Predicting Network Buffer Capacity for BBR Fairness ( Poster ) > link | Ibrahim Umit Akgun 路 Santiago Vargas 路 Andrew Burford 路 Michael McNeill 路 Michael Arkhangelskiy 路 Aruna Balasubramanian 路 Anshul Gandhi 路 Erez Zadok 馃敆 |
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LoopStack: ML-friendly ML Compiler Stack ( Poster - Recorded Presentation ) > link | Bram Wasti 路 Dejan Grubisic 路 Benoit Steiner 路 Aleksandar Zlateski 馃敆 |
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Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR ( Poster - Recorded Presentation ) > link | Sami Alabed 路 Dominik Grewe 路 Juliana Franco 路 Bart Chrzaszcz 路 Tom Natan 路 Tamara Norman 路 Norman Rink 路 Dimitrios Vytiniotis 路 Michael Schaarschmidt 馃敆 |
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Multi-objective Reinforcement Learning with Adaptive Pareto Reset for Prefix Adder Design ( Poster - Recorded Presentation ) > link | Jialin Song 路 Rajarshi Roy 路 Jonathan Raiman 路 Robert Kirby 路 Neel Kant 路 Saad Godil 路 Bryan Catanzaro 馃敆 |
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Preference-Aware Constrained Multi-Objective Bayesian Optimization For Analog Circuit Design ( Poster ) > link | Alaleh Ahmadianshalchi 路 Syrine Belakaria 路 Jana Doppa 馃敆 |
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The Case for Learning Machine Language
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Poster - Recorded Presentation
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link
SlidesLive Video |
Guangda Liu 路 Chieh-Jan Mike Liang 路 Shijie Cao 路 Shuai Lu 路 Leendert van Doorn 馃敆 |
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HloEnv: A Graph Rewrite Environment for Deep Learning Compiler Optimization Research ( Poster - Recorded Presentation ) > link | Chin Yang Oh 路 Kunhao Zheng 路 Bingyi Kang 路 Xinyi Wan 路 Zhongwen Xu 路 Shuicheng Yan 路 Min Lin 路 Yangzihao Wang 馃敆 |
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Robust Scheduling with GFlowNets
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Poster - Recorded Presentation
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link
SlidesLive Video |
David Zhang 路 Corrado Rainone 路 Markus Peschl 路 Roberto Bondesan 馃敆 |
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A code superoptimizer through neural Monte-Carlo tree search
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Poster - Recorded Presentation
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link
SlidesLive Video |
Wenda Zhou 路 Olga Solodova 路 Ryan Adams 馃敆 |
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Target-independent XLA optimization using Reinforcement Learning
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Poster - Recorded Presentation
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link
SlidesLive Video |
Milan Ganai 路 Haichen Li 路 Theodore Enns 路 Yida Wang 路 Randy Huang 馃敆 |
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Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration ( Poster ) > link | Srivatsan Krishnan 路 Natasha Jaques 路 Shayegan Omidshafiei 路 Dan Zhang 路 Izzeddin Gur 路 Vijay Janapa Reddi 路 Aleksandra Faust 馃敆 |
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An Efficient One-Class SVM for Novelty Detection in IoT
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Poster - Recorded Presentation
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link
SlidesLive Video |
Kun Yang 路 Samory Kpotufe 路 Nicholas Feamster 馃敆 |
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Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs ( Poster ) > link | Benjamin Fuhrer 路 Yuval Shpigelman 路 Chen Tessler 路 Shie Mannor 路 Gal Chechik 路 Eitan Zahavi 路 Gal Dalal 馃敆 |
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NeuralFuse: Improving the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
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Poster - Recorded Presentation
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link
SlidesLive Video |
Hao-Lun Sun 路 Lei Hsiung 路 Nandhini Chandramoorthy 路 Pin-Yu Chen 路 Tsung-Yi Ho 馃敆 |
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Lattice Quantization
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Poster - Recorded Presentation
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link
SlidesLive Video |
Cl茅ment Metz 路 Thibault Allenet 路 Johannes Thiele 路 Antoine DUPRET 路 Olivier BICHLER 馃敆 |
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An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design ( Poster - Recorded Presentation ) > link | Mingjie Liu 路 Haoyu Yang 路 David Pan 路 Brucek Khailany 路 Mark Ren 馃敆 |
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HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression ( Poster ) > link | Jiaqi Gu 路 Ben Keller 路 Jean Kossaifi 路 Anima Anandkumar 路 Brucek Khailany 路 David Pan 馃敆 |